class_docstr: Creates a Dataset for T5/BART architecture based Generative QA 
class_docstr_tok: ["Creates","a","Dataset","for","T5\/BART","architecture","based","Generative","QA"]
docstr: Formats a question into input format: ` question: <question text>`
  The space at the start allows concatention with the context for input
docstr_tok: ["Formats","a","question","into","input","format",":","`","question",":","<","question","text",">","`","The","space","at","the","start","allows","concatention","with","the","context","for","input"]
code: def _prep_query(self, example):
      """
      Formats a question into input format: ` question: <question text>`
      The space at the start allows concatention with the context for input
      """
      formatted_query = f" question: {example.question_text}"
      query_tokens = self.tokenizer.tokenizer.tokenize(formatted_query)[
          : self.max_query_length
      ]
  
      return query_tokens, formatted_query
  
file_path: NeMo/nemo/collections/nlp/data/question_answering/dataset/qa_s2s_dataset.py
imports: import os
  import numpy
  import torch
  from tqdm import trange
  from nemo.collections.nlp.data.question_answering.data_processor.qa_processing import INFERENCE_MODE, TRAINING_MODE
  from nemo.collections.nlp.data.question_answering.dataset.qa_dataset import QADataset
  from nemo.collections.nlp.data.question_answering.input_example.qa_s2s_input_example import S2SQAInputExample
  from nemo.utils import logging
label_desc: Use image node_id 4 for calling the S2SQADataset obj's underlying member method code with example usage: obj._prep_query(example) and returns: query_tokens,  formatted_query